ABSTRACT
A new leukocyte classification method for recognition of five types of human peripheral blood smear based on mean-shift clustering is proposed. The key idea of the proposed method is to extract the texture features of leukocytes in a visual manner which can benefit from human eyes. Firstly, some feature points are extracted in a gray leukocyte image by mean-shift. Secondly, these feature points are used as seeds of the region growing to expand feature regions which can express texture in visual mode to a certain extent. Finally, a parameter vector of these regions is extracted as the texture feature. Combing the vector with the geometric features of the leukocyte, the five typical classes of leukocytes can be recognized successfully using artificial neural network (ANN). A total number of 1 310 leukocyte images have been tested and the accurate rate of recognition for neutrophil, eosinophil, basophil, lymphocyte and monocyte are 95.4%, 93.8%, 100%, 93.1% and 92.4%, respectively, which shows the feasibility and high robustness of the proposed method.
ABSTRACT
Objective To investigate the expression of p53 in human ovarian neoplasms by tissue array technique Methods The expression of p53 protein in various ovarian tissues was studied by tissue array and immunohistochemistry Results The expression rate of p53 was 33% in ovarian cancers There were no expressions in normal ovarian tissues, benign ovarian neoplasms and borderline ovarian neoplasms( P 0 05) while p53 expression was associated with tissue types( P